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Identify shared differential genes

When conducting PCA analysis on the expression matrices of GSE33570 (Fig.2a) and GSE29315 (Fig.2d), we observed a clear two-sided distribution of samples in both the disease group and the control group. In the analysis of the GSE35570 dataset, a total of 1572 distinct genes were detected as being differentially expressed. These DEGs were categorized into 824 up-regulated genes and 748 down-regulated genes (Fig.2b). Similarly, we observed 423 DEGs in the GSE29315 dataset, including 271 up-regulated DEGs and 152 down-regulated DEGs (Fig.2e). Next, the GEGs of the two datasets are displayed heatmaps for both datasets (Fig.2c,f). Furthermore, we employed a Venn diagram to identify the overlapping genes with the same directional trend, resulting in 64 genes being up-regulated (Fig.2g) and 37 genes being down-regulated (Fig.2h).

Differential expression gene analysis, function enrichment analysis and pathway enrichment analysis. (a) The PCA plot of GSE35570. (b, c) The Volcano plot and heatmap of DEGs in GSE33570. (d) The PCA plot of GSE29315. (e, f) The Volcano plot and heatmap of DEGs in GSE29315. (g) Venn plot of the up-regulated DEGs. (h) Venn plot of the down-regulated DEGs. (i) The KEGG enrichment analyses of DEGs. (j) The GO enrichment analyses of DEGs.

In order to enhance our comprehension of the fundamental biological functions linked to the 101 DEGs, an assessment of GO and KEGG enrichment was conducted using the clusterProfiler software package in R. An analysis of GO highlighted that these shared genes were mainly enriched in leukocyte mediated immunity, myeloid leukocyte activation, and antigen processing and presentation (Fig.2j). Additionally, the DEGs exhibited significant enrichment across the top five KEGG pathways, including Tuberculosis, Phagosome, Viral myocarditis, Inflammatory bowel disease, and Th1 and Th2 cell differentiation (Fig.2i). Apparently, the functions of differentially expressed genes are closely associated with the immune function of the body. The core genes primarily serve the purpose of activating immune cells.

To carry out the PPI analysis, we utilized the STRING online tool and visualized the outcomes using the Cytoscape software (Supplementary Fig. S1a). The PPI network showed 68 nodes and 498 edges. The DC value of each node was calculated, with a median value of 11. Based on this, we identified 17 hub genes of PPI network: TYROBP, ITGB2, STAT1, HLA-DRA, C1QB, MMP9, FCER1G, IL10RA, LCP2, LY86, CD53, CD14, CD163, HCK, MNDA, HLA-DPA1, and ALOX5AP. Subsequently, we employed the MCODE plug-in to identify six modules (Supplementary Fig. S1b,c), which included a total of 29 common DEGs. These DEGs were LCP2, TYROBP, CD53, LY86, ITGB2, FCER1G, MNDA, C1QB, HCK, IL10RA, HLA-DRA, ALOX5AP, MT1G, MT1F, MT1E, MT1X, ISG15, IFIT3, PSMB9, GBP2, CD14, CD163, VSIG4, CAV1, TIMP1, S100A4, SDC2, FGFR2, and STAT1. The most important module comprises 12 genes (LCP2, TYROBP, CD53, LY86, ITGB2, FCER1G, MNDA, C1QB, HCK, IL10RA, HLA-DRA, ALOX5AP), which were further analyzed using the ClueGO plug-in in Cytoscape software. The investigation revealed that these genes primarily function in activating neutrophils to participate in the immune response and activating innate immunity (Supplementary Fig. S1d).

In this study, we analyzed a total of 26 genes from six modules extracted from MCODE. To determine the importance of each gene, we employed the RF algorithm in two datasets, namely GSE35570 (Fig.3a) and GSE29315 (Fig.3b). By comparing the rankings of gene importance in both datasets, we identified the top eight genes that were consistently ranked highly. To visualize this overlap, we created a Venn diagram (Fig.3c), which revealed three genes (CD53, FCER1G and TYROBP) that were shared between the two datasets. Remarkably, these three genes overlap with the hub genes identified through the PPI analysis based on DC values, as well as the genes found in the most significant module. These three genes showed promising diagnostic potential for HT and PTC. To evaluate the diagnostic value of the common hub genes, we computed the Cutoff Value, sensitivity, specificity, AUC and 95% CI for each gene in the four datasets (Table 1). In the GSE35570 dataset (Fig.3d), the AUC values were as follows: CD53 (AUC 0.71, 95% CI 0.610.82), FCER1G (AUC 0.81, 95% CI 0.730.89), and TYROBP (AUC 0.79, 95% CI 0.710.88). In the GSE29315 dataset (Fig.3e), the AUC values were as follows: CD53 (AUC 1.00, 95% CI 1.001.00), FCER1G (AUC 1.00, 95% CI 1.001.00) and TYROBP (AUC 1.00, 95% CI 1.001.00). In the TCGA dataset (Fig.3f), we validated the diagnostic value of the common hub genes for PTC. The AUC values were as follows: CD53 (AUC 0.71 95% CI 0.610.82), FCER1G (AUC 0.74, 95% CI 0.640.89) and TYROBP (AUC 0.80, 95% CI 0.700.89). To further evaluate the diagnostic value of the common hub genes for PTC in HT, we computed the AUC and 95% CI for each gene using GSE1398198. In the GSE138198 dataset (Fig.3g), the AUC values were as follows: CD53 (AUC 0.83, 95%CI 0.571.00), FCER1G (AUC 0.92, 95% CI 0.721.00) and TYROBP (AUC 1.00, 95% CI 1.001.00). We also analyzed the difference box plots between the two groups in the four datasets (Supplementary Fig. S2). Our analysis using box plots revealed a noteworthy disparity in gene expression between the HT group and the control group in GSE29315. This disparity serves as an explanation for the AUC values of the three hub genes in GSE29315, all of which were observed to be 1.

Screening of hub genes and the diagnostic value of hub genes. (a) The rankings of gene importance in GSE35570. (b) The rankings of gene importance in GSE29315. (c) Venn plot of the top eight genes in GSE35570 and GSE29315. (d) Diagnostic value of hub genes in the GSE35570. (e) Diagnostic value of hub genes in the GSE29315, (f) Diagnostic value of hub genes in the TCGA. (g) Diagnostic value of hub genes in the GSE138198.

By using the GSE35570 dataset, we developed three diagnostic model specifically for PTC, incorporating these pivotal genes that were identified through our analysis. The ANN model (Fig.4a) had 4 hidden units, a penalty of 0.0108, and was trained for 537 epochs. The ANN model achieved an AUC of 0.94 (95% CI 0.910.98) in the training set, while in the test set, the AUC was 0.94 (95% CI 0.831.00) (Fig.4b). The XGBoost model had 8 mtry, 6 min_n, 3 max_depth, 0.001 learn_rate, and 0.07 loss_reduction and 0.97 sample_size. The XGBoost model achieved an AUC of 0.84 (95% CI 0.750.93) in the training set, while in the test set, the AUC was 0.62 (95% CI 0.420.83) (Supplementary Fig. S3a). The DT model had 0.0003 cost_complexity, 5 tree_depth and 6 min_n. The DT model achieved an AUC of 0.93 (95% CI 0.900.97) in the training set, while in the test set, the AUC was 0.83 (95% CI 0.651.00) (Supplementary Fig. S3b). Supplementary Table S1 displays the predictive performance of three machine learning models. The results indicate that the ANN model outperformed the other models, leading us to choose the ANN model for further analysis. TCGA dataset as external validation dataset was utilized to assess the diagnostic performance of the ANN model for PTC, yielding an AUC value of 0.77 (95% CI 0.660.87) (Fig.4c). The GSE138198 dataset was used to evaluate the ANN models diagnostic efficacy for PTC in HT. In the GSE138198 dataset (Fig.4d), the ANN model demonstrated a perfect AUC of 1.00 (95% CI 1.001.00). To provide clinicians with a better understanding of variable contributions, we utilized the SHAP algorithm to interpret the ANN prediction results. Figure4e, f, g illustrated how the attributed importance of features changed as their values varied. Our findings reveal that CD53 had the most significant impact on the output of the ANN model. Initially, it was positively associated with the risk of PTC and then became negatively correlated after a turning point of approximately 6. TYROBP and FCER1G showed a positive correlation with the occurrence of PTC.

ANN model construction and feature importance analysis. (a) The ANN was constructed based on the shared hub genes. (b) Diagnostic value of the ANN model in the GSE35570. (c) Diagnostic value of the ANN model in the TCGA. (d) Diagnostic value of the ANN model in the GSE138198. (e) A score calculated by SHAP was used for each input feature. (f, g) Distribution of the impact of each feature on the full model output estimated using the SHAP values.

We analyzed the protein expression of the hub genes based on the HPA database (Supplementary Fig. S4). CD53 was highly expressed in both tumor and normal tissues, while FCER1G and TYROBP showed higher expression in tumors compared to normal tissues. Furthermore, IF staining was performed to measure the expressions of CD53, FCER1G, and TYROBP in our clinical samples, including 10 HT-related PTC tissues and 6 NAT. By performing IF analysis (Fig.5), we obtained semi-quantitative results indicating significantly elevated fluorescence signal intensities for CD53, FCER1G, and TYROBP in the HT-related PTC group, as compared to the NAT group (P<0.05).

Microscopy scan of IF staining showed the distribution of CD53(green), FCER1G(green), and TYROBP(green), in HT-related PTC tissues and normal tissues adjacent to the tumour (NAT); as well as diagnostic value of CD53, FCER1G and TYROBP. MFI: Mean Fluorescence Intensity.

Considering the important roles of immune and inflammatory responses in the development of HT and PTC, we analyzed the differences in immune cell infiltration patterns between PTC, HT and normal samples using the CIBERSORT algorithm. By utilizing the GSE35570 dataset, we identified 12 immune subgroups that exhibited significant variations between PTC and normal samples (Supplementary Fig. S5a). Additionally, the analysis of the GSE29315 dataset revealed 5 immune subgroups that were significantly different between HT and normal samples (Supplementary Fig. S5b). Among these, 4 common immune subpopulations were found to be significantly higher in both PTC and HT samples compared to normal samples. These subpopulations included T cells CD8, T cells CD4 memory resting, macrophages M1 and mast cells resting. Additionally, we conducted spearman correlation analysis between hub genes and immune cells (Supplementary Fig. S5c,d). The results suggested that immune responses could potentially contribute to the involvement of hub genes in PTC and HT progression. IF staining was utilized to identify immune cell infiltration in 5 cases of PTC in HT tissues and 5 cases of NAT (Fig.6). The expression levels of CD4+T-cell marker Cd4, CD8+T-cell marker Cd8, and macrophage marker Cd86 were found to be significantly higher in the PTC in HT group compared to the NAT group. The IF staining results provided some extent of verification for the accuracy of the immune infiltration analysis results.

Microscopy scan of IF staining showed the distribution of Cd4(green), Cd8(green), and Cd86(green), in HT-related PTC tissues and normal tissues adjacent to the tumour (NAT). MFI: Mean Fluorescence Intensity.

Based on the three core genes screened in the RF algorithm, we conducted a search in the DGIdb database for relevant potential drugs. The results showed that only FCER1G had relevant drugs, while no relevant drugs were found for CD53 and TYROBP. FCER1G was predicted to have two potential drugs: benzylpenicilloyl polylysine and aspirin. Among these, benzylpenicilloyl polylysine had the highest score of 29.49, while aspirin had a score of only 1.26. We hypothesise that benzylpenicilloyl polylysine and aspirin may be effective in the treatment of HT and PTC and may prevent HT carcinogenesis.

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Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis ... - Nature.com

While buzz about artificial intelligence (AI) has largely focused on the growing popularity of generative AI tools such as ChatGPT, the demand for jobs and growth in the sector is booming. In fact, AI and machine learning specialist roles are growing faster than any other occupation in the world, according to the World Economic Reports Future of Jobs Report.

Ryan Aytay, CEO of Tableau, says AI and big datas rapid growth in popularity and growth has created a need for everyone to learn the appropriate skills as well as to more broadly adopt a philosophy of lifelong learning.

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[AI] only seems to have accelerated this need for everyone, not just business users, not just analysts, really everyone to have the ability to not only see and understand but also use that data to make decisions with regardless of what they need to be focused on, Aytay says.

Over the past few months, more universities have sought to meet the AI demand head on by creating degree programs specifically focused on the subject. For example, just in March 2024, Purdue Universitya school known for its strong engineering armannounced a brand new online masters in AI.

If AI from a business perspective interests you, youre in luck, too. Many business schools now offer MBA specializations in AI as well as certifications focused on the subject.

And while there are also options to take free online courses in artificial intelligence, many schools now offer full-fledged degree tracks. Fortune compiled a list of six masters in AI programs to check out if youre looking to make a career switch.

At Duke University, students in the artificial intelligence for product innovation master of engineering program can complete courses in -person in 12 to 16 months or online within 24 months. Students can also choose from a variety of learning tracksor a focusincluding data science and machine learning.

The program also includes a capstone project and summer internship. Graduates often move intotake jobs as machine learning engineers, AI engineers, data scientists, and data engineers for companies including OpenAI, Doordash, and Targets AI Lab within six months of graduation. All students must complete an online data science and Python bootcamp the summer before the start of their program.

Students complete 10 courses during the program, covering topics including AI, machine learning, operations, and management. The management courses are offered through Dukes Law School and Fuqua School of Business, which Fortune ranks as having one of the top full-time MBA programs in the U.S.

Applicants are expected to have an undergraduate degree in science or engineering (or equivalent technical work experience), minimum one year of programming experience, two semesters completed of calculus, and meet English proficiency admission requirements (for international students).

The cost of Dukes program depends on the modality (online or in-person) and the amount of time taken to complete the degree. Applications require transcripts, short-answer essay responses, a resume, three letters of recommendation, and an introductory video. Prospective students have the option to submit GRE scores.

Format: Online or in-person

Cost: $99,734 (online); $113,892 (in-person)

Deadlines: Round 1: January 15 (online and in-person); Round 2: March 15 (in-person), April 15 (online)

Johns Hopkins University offers both a masters degree and a graduate certificate in artificial intelligence through its Whiting School of Engineering. The online masters in AI includes 10 coursesfour core courses and six electivesand students can take up to five years to complete them.

Curriculum includes algorithms, applied machine learning, and creating AI-enabled systems. Johns Hopkins does require several prerequisite courses including calculus, programming, and linear algebra, but will offer provisional admission for students to complete the required courses prior to enrollment.

GRE scores arent required to apply, but most admitted students have at least a 3.0 undergraduate GPA.

Format: Online

Cost: $52,700 (estimated total program price)

Deadlines: Open year-round (terms begin in spring, summer, and fall)

Northwestern Universitys masters in artificial intelligence seeks to train those with a desire to become architects of intelligent systems. Through the program, students learn the psychological and design implications of AI and how business needs may be satisfied.

Students can take a traditional track or choose the MSAI+X program and combine AI with their original field of study. The program is limited to approximately 40 students per year and lasts for 15 months.

Applicants should have a bachelors in computer science or related field, and preference will be given to those with at least two years of relevant work experience.

Format: In-person

Cost: ~$110,000

Deadlines: December 15 (priority); March 15 (final)

Purdues new masters in artificial intelligence seeks to prepare students to succeed in todays increasingly tech-reliant world. Students will learn practical skills in AI and computing as well as professional skills like leadership and project management and technical skills like programming and machine learning.

Participants can choose two major tracks: AI and machine learning or AI management and policy. Admissions requirements differ depending on which major is chosen. There is no application fee to apply. While English proficiency testing is required for international students, GRE and GMAT scores are not needed.

Format: Online

Cost: ~$28,000

Deadlines: August 1 (fall); December 1 (spring); April 1 (summer)

The masters in AI at the University of MichiganDearborn teaches students the foundational theory and practice of AI. The program is very flexibility in the sense that students can choose to learn online, in-person, or hybrid, and learn either on a full- or part-time basis. Because of the latter offering, courses are hosted in the late afternoon or evening hours.

Students can focus on four different concentrations: computer vision, intelligence interaction, machine learning, or knowledge management and reasoning. Admission into the program requires students to have graduated with bachelors degree in a STEM field with a B average. Mathematics skills, such as calculus III and linear algebra, is recommended but not required.

Format: Online, in-person, or hybrid

Cost: $50,208/year (direct + indirect costs, out-of-state)

Deadlines: Rolling admission

UTAustin offers its online masters program in AI through its department of computer science and machine learning laboratory, and the degree can be completed at your own pace. The degree covers about two years worth of content. The program is offered on the online education platform, edX, an online education platform, and costs $10,000 to complete, making it one of the more affordable options.

The degree covers AI-related topics, including natural language processing, reinforcement learning, computer vision, and deep learning, which prepares graduates for A.I. jobs in engineering, research and development, product management, and consulting.

The program quickly skyrocketed in popularity, with more than 4,000 prospective students requesting more information from the university within 24 hours of its launch announcement.

Prospective students must submit an application to the Graduate School at The University of Texas at Austin as well as a statement of purpose, resume, and transcripts. Letters of recommendation and GRE scores are optional to submit.

Format: Online

Cost: $10,000 (202324 academic year)

Deadlines: Fall: April 1 (priority), May 1 (final); Spring: August 15 (priority), September 15 (final)

Yes, having a masters in AI can be very beneficial for those wanting to become AI experts. However, it is also important to keep in mind that AI is always evolving. By the time your program completes, some of the skills and best practices you initially learned could be out of date.

Yes, you will need to learn how to code if you plan to study AI in an advanced degree program. Python is generally considered to be the most relevant programming language to AI. Having skills in Java, SQL, C++, and R also couldnt hurt. Some masters in AI programs, like Duke, require students to have some programming experience as well as to enroll in a Python bootcamp.

The best degree pathway for those interested in AI truly depends on your interests.

A masters in AI will likely give you a perfect entry into careers in AI, data science, machine learning, and beyond. If you know a particular specialization in the tech space interests you more than another, that is a great place to start. Above all, keep in mind that because AI masters are new, there is no perfect path; its up to you to define it.

Check out all of Fortunes rankings of degree programs, and learn more about specific career paths.

Sydney Lake contributed to this piece.

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Looking to break into AI? These 6 schools offer master's in artificial intelligence programs - Fortune

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You have likely already had the opportunity to interact with generative artificial intelligence (AI) tools (such as virtual assistants and chatbot applications) and noticed that you dont always get the answer you are looking for, and that achieving it may not be straightforward. Large language models (LLMs), the models behind the generative AI revolution, receive instructions on what to do, how to do it, and a set of expectations for their response by means of a natural language text called a prompt. The way prompts are crafted greatly impacts the results generated by the LLM. Poorly written prompts will often lead to hallucinations, sub-optimal results, and overall poor quality of the generated response, whereas good-quality prompts will steer the output of the LLM to the output we want.

In this post, we show how to build efficient prompts for your applications. We use the simplicity of Amazon Bedrock playgrounds and the state-of-the-art Anthropics Claude 3 family of models to demonstrate how you can build efficient prompts by applying simple techniques.

Prompt engineering is the process of carefully designing the prompts or instructions given to generative AI models to produce the desired outputs. Prompts act as guides that provide context and set expectations for the AI. With well-engineered prompts, developers can take advantage of LLMs to generate high-quality, relevant outputs. For instance, we use the following prompt to generate an image with the Amazon Titan Image Generation model:

An illustration of a person talking to a robot. The person looks visibly confused because he can not instruct the robot to do what he wants.

We get the following generated image.

Lets look at another example. All the examples in this post are run using Claude 3 Haiku in an Amazon Bedrock playground. Although the prompts can be run using any LLM, we discuss best practices for the Claude 3 family of models. In order to get access to the Claude 3 Haiku LLM on Amazon Bedrock, refer to Model access.

We use the following prompt:

Claude 3 Haikus response:

The request prompt is actually very ambiguous. 10 + 10 may have several valid answers; in this case, Claude 3 Haiku, using its internal knowledge, determined that 10 + 10 is 20. Lets change the prompt to get a different answer for the same question:

Claude 3 Haikus response:

The response changed accordingly by specifying that 10 + 10 is an addition. Additionally, although we didnt request it, the model also provided the result of the operation. Lets see how, through a very simple prompting technique, we can obtain an even more succinct result:

Claude 3 Haiku response:

Well-designed prompts can improve user experience by making AI responses more coherent, accurate, and useful, thereby making generative AI applications more efficient and effective.

The Claude 3 family is a set of LLMs developed by Anthropic. These models are built upon the latest advancements in natural language processing (NLP) and machine learning (ML), allowing them to understand and generate human-like text with remarkable fluency and coherence. The family is comprised of three models: Haiku, Sonnet, and Opus.

Haiku is the fastest and most cost-effective model on the market. It is a fast, compact model for near-instant responsiveness. For the vast majority of workloads, Sonnet is two times faster than Claude 2 and Claude 2.1, with higher levels of intelligence, and it strikes the ideal balance between intelligence and speedqualities especially critical for enterprise use cases. Opus is the most advanced, capable, state-of-the-art foundation model (FM) with deep reasoning, advanced math, and coding abilities, with top-level performance on highly complex tasks.

Among the key features of the models family are:

To learn more about the Claude 3 family, see Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together, Anthropics Claude 3 Sonnet foundation model is now available in Amazon Bedrock, and Anthropics Claude 3 Haiku model is now available on Amazon Bedrock.

As prompts become more complex, its important to identify its various parts. In this section, we present the components that make up a prompt and the recommended order in which they should appear:

The following is an example of a prompt that incorporates all the aforementioned elements:

In the following sections, we dive deep into Claude 3 best practices for prompt engineering.

For prompts that deal only with text, follow this set of best practices to achieve better results:

The Claude 3 family offers vision capabilities that can process images and return text outputs. Its capable of analyzing and understanding charts, graphs, technical diagrams, reports, and other visual assets. The following are best practices when working with images with Claude 3:

Consider the following example, which is an extraction of the picture a fine gathering (Author: Ian Kirck, https://en.m.wikipedia.org/wiki/File:A_fine_gathering_(8591897243).jpg).

We ask Claude 3 to count how many birds are in the image:

Claude 3 Haikus response:

In this example, we asked Claude to take some time to think and put its reasoning in an XML tag and the final answer in another. Also, we gave Claude time to think and clear instructions to pay attention to details, which helped Claude to provide the correct response.

Lets see an example with the following image:

In this case, the image itself is the prompt: Claude 3 Haikus response:

Lets look at the following example:

Prompt:

Claude 3 Haikus response:

Lets see an example. We pass to Claude the following map chart in image format (source: https://ourworldindata.org/co2-and-greenhouse-gas-emissions), then we ask about Japans greenhouse gas emissions.

Prompt:

Claude 3 Haikus response:

Lets see an example of narration with the following image (source: Sustainable Development Goals Report 2023, https://unstats.un.org/sdgs/report/2023/The-Sustainable-Development-Goals-Report-2023.pdf):

Prompt:

Claude 3 Haikus response:

In this example, we were careful to control the content of the narration. We made sure Claude didnt mention any extra information or discuss anything it wasnt completely confident about. We also made sure Claude covered all the key details and numbers presented in the slide. This is very important because the information from the narration in text format needs to be precise and accurate in order to be used to respond to questions.

Information extraction is the process of automating the retrieval of specific information related to a specific topic from a collection of texts or documents. LLMs can extract information regarding attributes given a context and a schema. The kinds of documents that can be better analyzed with LLMs are resumes, legal contracts, leases, newspaper articles, and other documents with unstructured text.

The following prompt instructs Claude 3 Haiku to extract information from short text like posts on social media, although it can be used for much longer pieces of text like legal documents or manuals. In the following example, we use the color code defined earlier to highlight the prompt sections:

Claude 3 Haikus response:

The prompt incorporates the following best practices:

Retrieval Augmented Generation (RAG) is an approach in natural language generation that combines the strengths of information retrieval and language generation models. In RAG, a retrieval system first finds relevant passages or documents from a large corpus based on the input context or query. Then, a language generation model uses the retrieved information as additional context to generate fluent and coherent text. This approach aims to produce high-quality and informative text by using both the knowledge from the retrieval corpus and the language generation capabilities of deep learning models. To learn more about RAG, see What is RAG? and Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart.

The following prompt instructs Claude 3 Haiku to answer questions about a specific topic and use a context from the retrieved information. We use the color code defined earlier to highlight the prompt sections:

Claude 3 Haikus response:

The prompt incorporates the following best practices:

In this post, we explored best prompting practices and demonstrated how to apply them with the Claude 3 family of models. The Claude 3 family of models are the latest and most capable LLMs available from Anthropic.

We encourage you to try out your own prompts using Amazon Bedrock playgrounds on the Amazon Bedrock console, and try out the official Anthropic Claude 3 Prompt Engineering Workshop to learn more advanced techniques. You can send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Refer to the following to learn more about the Anthropic Claude 3 family:

David Laredo is a Prototyping Architect at AWS, where he helps customers discover the art of the possible through disruptive technologies and rapid prototyping techniques. He is passionate about AI/ML and generative AI, for which he writes blog posts and participates in public speaking sessions all over LATAM. He currently leads the AI/ML experts community in LATAM.

Claudia Cortes is a Partner Solutions Architect at AWS, focused on serving Latin American Partners. She is passionate about helping partners understand the transformative potential of innovative technologies like AI/ML and generative AI, and loves to help partners achieve practical use cases. She is responsible for programs such as AWS Latam Black Belt, which aims to empower partners in the Region by equipping them with the necessary knowledge and resources.

Simn Crdova is a Senior Solutions Architect at AWS, focused on bridging the gap between AWS services and customer needs. Driven by an insatiable curiosity and passion for generative AI and AI/ML, he tirelessly explores ways to leverage these cutting-edge technologies to enhance solutions offered to customers.

Gabriel Velazquez is a Sr Generative AI Solutions Architect at AWS, he currently focuses on supporting Anthropic on go-to-market strategy. Prior to working in AI, Gabriel built deep expertise in the telecom industry where he supported the launch of Canadas first 4G wireless network. He now combines his expertise in connecting a nation with knowledge of generative AI to help customers innovate and scale.

Originally posted here:
Prompt engineering techniques and best practices: Learn by doing with Anthropic's Claude 3 on Amazon Bedrock ... - AWS Blog

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PND-Net: plant nutrition deficiency and disease classification using graph convolutional network | Scientific Reports - Nature.com